LOCALIZATION FOR THE NEW RESTAURANT IN PHOENIX

The purpose of this notebook is to discover a good location for the new restaurant in Phoenix. We will approach this analysis both considering success and number of reviews of existing restaurant in Phoenix and evaluating the competence this new restaurant chain will have in the city.

import pandas as pd
import folium
from folium import plugins
from folium.plugins import HeatMap
import numpy as np
import matplotlib.pyplot as plt

Localization based on existing data from restaurants in Phoenix

First, we load the data from cleaned_restaurants.csv and filter it by the metropolis of Phoenix.

cleaned_restaurants=pd.read_csv('cleaned_restaurants.csv')

p_metro=cleaned_restaurants[restaurants['state']=='AZ']
p_metro=p_metro[p_metro['latitude']<34]

We will create an objective function in order to catalogue both the success (stars) and number of reviews of the restaurants in phoenix. The function will ponderate by 30% the number of stars normalised and by 70% the number of reviews normalised as we consider more important for a good restaurant prioritize in number of potential customers.

p_metro['objective']=0.3*p_metro['stars']/max(p_metro['stars'])+0.7*p_metro['review_count']/max(p_metro['review_count'])

modeldata=pd.DataFrame()

modeldata['lat']=p_metro['latitude']
modeldata['lon']=p_metro['longitude']

modeldata['rating']=p_metro['stars']#.apply(lambda x: '1' if x>=4 else 0)
modeldata['reviews']=p_metro['review_count']

modeldata.head()

	lat	lon	rating	reviews
5	33.602822	-111.983533	4.0	10
11	33.393199	-111.976270	4.0	9
12	33.640391	-112.187028	3.5	12
14	33.394863	-111.756036	3.0	13
16	33.350104	-111.963660	3.0	4

modeldata['objective']=0.3*modeldata['rating']/max(modeldata['rating'])+0.7*modeldata['reviews']/max(modeldata['reviews'])

modeldata.describe()

	lat	lon	rating	reviews	objective
count	12124.000000	12124.000000	12124.000000	12124.000000	12124.000000
mean	33.487799	-112.001729	3.428819	114.427994	0.228517
std	0.121441	0.168255	0.827837	192.675892	0.069731
min	33.210781	-112.540321	1.000000	3.000000	0.060597
25%	33.394509	-112.103986	3.000000	15.000000	0.183186
50%	33.479974	-111.978183	3.500000	46.000000	0.227760
75%	33.582542	-111.892342	4.000000	135.250000	0.271394
max	33.844710	-111.600936	5.000000	3515.000000	0.940000

We will now just plot using a heatmap, those restaurants with an objective value higher than 0.55. That would mean that we will just plot those restaurants with the desired combination of reviews and stars.

from folium import plugins
from folium.plugins import HeatMap


heatmap= folium.Map(location=[33.451, -112.0578],
                    zoom_start = 11) 

# Ensure you're handing it floats
modeldataheat=modeldata.copy()
modeldataheat['lat'] = (modeldataheat['lat']).astype(float)
modeldataheat['lon'] = (modeldataheat['lon']).astype(float)


heat_df = modeldataheat[modeldataheat.objective>0.55] 
heat_df = heat_df[['lat', 'lon']]
heat_df = heat_df.dropna(axis=0, subset=['lat','lon'])

# List comprehension to make out list of lists
heat_data = [[row['lat'],row['lon']] for index, row in heat_df.iterrows()]

# Plot it on the map
HeatMap(heat_data).add_to(heatmap)

# Display the map
heatmap

from IPython.display import Image
Image("C:/Users/hecto.DESKTOP-ACA6A3T/Documents/DENMARK/BANALYTICS/2semester/Advance BA/Final Project/FINAL FILES/localization_1.PNG")

As we can see in the heatmap, the best businesses in the metropolis of Phoenix are located in the city center of Phoenix, Scottdale and Tempe. For a first restaurant, it would be better to place it in the city center of Phoenix since it has more customers and visibility.

Potential Competitors of Banh Mi Boys in Phoenix

Throughout this part, since we have the target customers in Phoenix, we want to visualize where they usually go in order to place the new restaurant next to those places.

business=pd.read_csv('reviews_rest_phoenix_metropolis.csv')
Target_users=pd.read_csv('future_customers.csv')
restaurants=business.merge(cleaned_restaurants,on='business_id',how='left')
restaurants=restaurants[restaurants['categories'].notnull()==True]

customers=Target_users['user_id'].unique()
List_restaurants=(restaurants.groupby(['business_id']).count())
List_restaurants=List_restaurants.rename(columns={'cool': 'Number of reviews'})
List_restaurants=List_restaurants['Number of reviews']
List_restaurants=pd.DataFrame(List_restaurants)
List_restaurants=List_restaurants.reset_index(level=['business_id'])
List_business_id=List_restaurants['business_id']

We will analyze in which restaurants the target users are going, it will inform us, in which area they are likely to go in Phoenix

frequency=[]


for B_id in List_business_id:
    search=restaurants[restaurants['business_id']==B_id]
    users_search=search['user_id'].unique()
    
    rate=0
    for user in users_search:
        if user in customers:
            rate=rate+1
    
    frequency.append(rate)

The frequency, informs us, how many people of our sample of customers have been in each restaurant of Phoenix

List_restaurants['frequency']=frequency
List_restaurants=List_restaurants.sort_values(by=['frequency'],ascending= False )
List_restaurants.head(25)

	business_id	Number of reviews	frequency
8454	gQMAcDm8kv8ev7x2BshMwg	944	6
4074	JzOp695tclcNCNMuBl7oxA	2473	6
1777	85o8XAZDoPmRqHFhdJLAGg	343	6
5109	PhMfnMldVKXo7HqHw6DSrg	372	5
4931	OgJ0KxwJcJ9R5bUK0ixCbg	2284	5
10942	thLX_k20SPJ0KyusGTBIHw	1485	5
8063	eS29S_06lvsDW04wVrIVxg	1570	5
5901	U1ZVgF-kfkvv_rcoe0RglQ	313	5
7704	cTJjTKz2huGZ-ElScC2pSw	1280	5
11502	wl0QZqAzr1DelslQ02JGCQ	1728	4
1491	6ZIHxvFTHC1pvAzAS0uLDA	559	4
2582	C8D_GU9cDDjbOJfCaGXxDQ	1768	4
3535	HGuHk935tyokX6kveEviJw	366	4
306	0HgLKI1T9WT3hJXskDtUrQ	248	4
4802	NxjUiGBNgyDErKXnmQ3s3w	800	4
168	-kIscN8I29eXMPkvyyxmRQ	886	4
3020	EV1jAj7NFNmNIqOnwZ4Acw	650	4
1025	46yg8fJ8fVA1QSaaL7XfOw	361	4
6019	UY13CTgUqriT-fMxT2APLw	493	4
7134	_XN-GwzZwAyIqLKJsl2htg	1707	4
10091	p81LV6y6Jxz_q_3HL06jOw	435	3
4953	OoYwJANV9zD-_OA4Atu-gg	730	3
8250	fKlr9qNoV37WQJwQ826POg	1470	3
11809	yNPh5SO-7wr8HPpVCDPbXQ	2399	3
671	2GmGT-7QjowR1ihup3FbVA	896	3

We will only consider the restaurants where our target users have been

List_restaurants2=List_restaurants[List_restaurants['frequency']>0]
List_restaurants2=List_restaurants2.reset_index(drop=True)
List_restaurants2.head()

	business_id	Number of reviews	frequency
0	gQMAcDm8kv8ev7x2BshMwg	944	6
1	JzOp695tclcNCNMuBl7oxA	2473	6
2	85o8XAZDoPmRqHFhdJLAGg	343	6
3	PhMfnMldVKXo7HqHw6DSrg	372	5
4	OgJ0KxwJcJ9R5bUK0ixCbg	2284	5

List_restaurants2.describe()

	Number of reviews	frequency
count	1208.000000	1208.000000
mean	382.567881	1.264073
std	406.825246	0.659367
min	5.000000	1.000000
25%	119.000000	1.000000
50%	264.500000	1.000000
75%	493.250000	1.000000
max	3608.000000	6.000000

Let's put the localisation of these restaurants.

List_business_id2=List_restaurants2['business_id']
Lat=[]
Long=[]


for B_id in List_business_id2:
    search=restaurants[restaurants['business_id']==B_id]
    lat=search['latitude'].reset_index(drop = True)
    long=search['longitude'].reset_index(drop = True)

    Lat.append(lat[0])
    Long.append(long[0])

List_restaurants2['latitude']=Lat
List_restaurants2['longitude']=Long

List_restaurants2.head()

	business_id	Number of reviews	frequency	latitude	longitude
0	gQMAcDm8kv8ev7x2BshMwg	944	6	33.510181	-112.096168
1	JzOp695tclcNCNMuBl7oxA	2473	6	33.419578	-111.915885
2	85o8XAZDoPmRqHFhdJLAGg	343	6	33.478016	-112.254559
3	PhMfnMldVKXo7HqHw6DSrg	372	5	33.394818	-111.940576
4	OgJ0KxwJcJ9R5bUK0ixCbg	2284	5	33.500597	-112.074237

We can now calculate the wheighted average longitude and latitde of these restaurants.

List_restaurants3=List_restaurants2.copy()
List_restaurants3['lat2']=List_restaurants3['frequency']*List_restaurants3['latitude']
mean_lat=List_restaurants3['lat2'].sum()/List_restaurants3['frequency'].sum()
print(mean_lat)
List_restaurants3['long2']=List_restaurants3['frequency']*List_restaurants3['longitude']
mean_long=List_restaurants3['long2'].sum()/List_restaurants3['frequency'].sum()
print(mean_long)

33.490570678962804
-111.99894202453397

Let's now look at all of the restaurants in Phoenix The file Business_Metropolis can be downloaded by running the notebook: DescripitveAnalysis Yelp Dataset inside: https://github.com/hecmesge/ABA2020

Business=pd.read_excel('Business-Metropolis.xlsx')
Business=Business[Business['categories'].notnull()==True]
Restaurants=Business[Business['categories'].str.contains('Restaurants')]
Restaurants=Restaurants[Restaurants['metropolis']=='Phoenix']
Restaurants.head()

	RestaurantsTakeOut	BusinessAcceptsCreditCards	NoiseLevel	GoodForKids	RestaurantsReservations	RestaurantsGoodForGroups	RestaurantsPriceRange2	HasTV	Alcohol	BikeParking	...	is_open	latitude	longitude	name	postal_code	review_count	stars	state	metropolis	distance metropolis
877	1	1	0	1	0	1	1	0	0	1	...	1	33.602822	-111.983533	Wetzel's Pretzels	85032	10	4.0	AZ	Phoenix	0.0
878	1	1	0	1	0	1	1	0	0	1	...	1	33.458972	-112.047940	Church's Chicken	85006	5	3.5	AZ	Phoenix	0.0
879	1	1	-1	1	0	1	1	0	0	0	...	0	33.567452	-112.073536	Sam's Gyros	85020	9	4.0	AZ	Phoenix	0.0
880	1	1	0	1	0	1	1	1	0	1	...	0	33.611785	-112.110520	Asian Cafe	85023	49	4.0	AZ	Phoenix	0.0
881	1	1	0	1	1	1	2	1	0	0	...	1	33.466146	-112.119726	Tacos y Mariscos Sinaloa	85009	14	3.0	AZ	Phoenix	0.0

5 rows × 68 columns

List_business_id2=List_restaurants2['business_id']
Target_Restaurants=Restaurants[Restaurants['business_id'].isin(List_business_id2)]
Target_Restaurants.describe()

	RestaurantsTakeOut	BusinessAcceptsCreditCards	NoiseLevel	GoodForKids	RestaurantsReservations	RestaurantsGoodForGroups	RestaurantsPriceRange2	HasTV	Alcohol	BikeParking	...	dessert	dinner	latenight	lunch	is_open	latitude	longitude	review_count	stars	distance metropolis
count	1168.000000	1168.000000	1168.000000	1168.000000	1168.000000	1168.000000	1168.000000	1168.000000	1168.000000	1168.000000	...	1168.000000	1168.000000	1168.000000	1168.000000	1168.000000	1168.000000	1168.000000	1168.000000	1168.000000	1168.000000
mean	0.957192	0.992295	-0.025685	0.858733	0.407534	0.913527	1.664384	0.721747	1.069349	0.845890	...	0.068493	0.613014	0.054795	0.684075	0.823630	33.493301	-112.001896	376.785103	3.778682	13.029105
std	0.202511	0.087479	0.425443	0.348446	0.491586	0.281181	0.551106	0.448331	0.903854	0.361208	...	0.252698	0.487269	0.227676	0.465082	0.381298	0.107776	0.135021	395.798837	0.574776	11.398203
min	0.000000	0.000000	-1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	33.246440	-112.415503	6.000000	1.500000	0.000000
25%	1.000000	1.000000	0.000000	1.000000	0.000000	1.000000	1.000000	0.000000	0.000000	1.000000	...	0.000000	0.000000	0.000000	0.000000	1.000000	33.421592	-112.074015	120.000000	3.500000	0.000000
50%	1.000000	1.000000	0.000000	1.000000	0.000000	1.000000	2.000000	1.000000	1.000000	1.000000	...	0.000000	1.000000	0.000000	1.000000	1.000000	33.494221	-111.970399	263.000000	4.000000	14.809270
75%	1.000000	1.000000	0.000000	1.000000	1.000000	1.000000	2.000000	1.000000	2.000000	1.000000	...	0.000000	1.000000	0.000000	1.000000	1.000000	33.578296	-111.922255	482.000000	4.000000	18.964006
max	1.000000	1.000000	2.000000	1.000000	1.000000	1.000000	4.000000	1.000000	2.000000	1.000000	...	1.000000	1.000000	1.000000	1.000000	1.000000	33.832191	-111.682255	3515.000000	5.000000	35.132503

8 rows × 55 columns

Target_Restaurants.head()

	RestaurantsTakeOut	BusinessAcceptsCreditCards	NoiseLevel	GoodForKids	RestaurantsReservations	RestaurantsGoodForGroups	RestaurantsPriceRange2	HasTV	Alcohol	BikeParking	...	is_open	latitude	longitude	name	postal_code	review_count	stars	state	metropolis	distance metropolis
883	1	1	0	1	1	1	2	1	2	1	...	1	33.479910	-111.982293	Garcia's	85018	139	3.0	AZ	Phoenix	0.0
888	1	1	0	1	1	1	2	1	2	0	...	1	33.683465	-111.965897	stonegrill	85054	89	3.5	AZ	Phoenix	0.0
893	1	1	0	0	0	1	2	1	2	1	...	0	33.471988	-112.064807	Rice Paper	85006	445	3.5	AZ	Phoenix	0.0
914	1	1	0	1	0	1	2	1	2	0	...	1	33.510988	-112.133277	Great Wall Cuisine	85017	557	3.5	AZ	Phoenix	0.0
918	1	1	0	1	0	1	2	0	1	1	...	1	33.510484	-111.986166	Flower Child	85018	557	4.0	AZ	Phoenix	0.0

5 rows × 68 columns

restaurants_Phoenix_target=Target_Restaurants[Target_Restaurants['distance metropolis']<=90].reset_index(drop = True)
long_target=restaurants_Phoenix_target['longitude']
lat_target=restaurants_Phoenix_target['latitude']

Restaurants only contain all of the restaurants in Phoenix

restaurants_Phoenix=Restaurants[Restaurants['distance metropolis']<=90].reset_index(drop = True)

long_P=restaurants_Phoenix['longitude']
lat_P=restaurants_Phoenix['latitude']

This map shows all of the restaurants in Phoenix (in black), all of the restaurants where our target users have been (in cyan) and the weighted average localisation of these restaurants (regarding the frequency of which our target users have been in these restaurants) in red.

# Phoenix location
lat_Phoenix = 33.4483771
lon_Phoenix = -112.0740373

map_P = folium.Map([lat_Phoenix, lon_Phoenix], zoom_start=10)

for i in range(len(long_P)):
    # Circle marker
    folium.CircleMarker([lat_P[i],long_P[i]], radius=1, color='black').add_to(map_P)
    
for i in range(len(long_target)):
    # Circle marker
    folium.CircleMarker([lat_target[i],long_target[i]], radius=1, color='cyan').add_to(map_P)

folium.CircleMarker([mean_lat,mean_long], radius=3, color='red').add_to(map_P)
    
    
    
map_P

from IPython.display import Image
Image("C:/Users/hecto.DESKTOP-ACA6A3T/Documents/DENMARK/BANALYTICS/2semester/Advance BA/Final Project/FINAL FILES/heat_1.JPG")

The following map is a heat map of the restaurants where our target users have been.

# Phoenix location
lat_Phoenix = 33.4483771
lon_Phoenix = -112.0740373

map_Phoenix = folium.Map([lat_Phoenix, lon_Phoenix], zoom_start=10)


for index, row in List_restaurants2.iterrows():
    folium.CircleMarker([row['latitude'], row['longitude']],
                        radius=row['frequency'],
                        fill_color="#3db7e4", # divvy color
                       ).add_to(map_Phoenix)
    
# convert to (n, 2) nd-array format for heatmap
matrix = List_restaurants2[['latitude', 'longitude']].as_matrix()

# plot heatmap
map_Phoenix.add_children(plugins.HeatMap(matrix, radius=15))
map_Phoenix

C:\Users\hecto.DESKTOP-ACA6A3T\Anaconda3\lib\site-packages\ipykernel_launcher.py:15: FutureWarning:

Method .as_matrix will be removed in a future version. Use .values instead.

C:\Users\hecto.DESKTOP-ACA6A3T\Anaconda3\lib\site-packages\ipykernel_launcher.py:18: FutureWarning:

Method `add_children` is deprecated. Please use `add_child` instead.

Image("C:/Users/hecto.DESKTOP-ACA6A3T/Documents/DENMARK/BANALYTICS/2semester/Advance BA/Final Project/FINAL FILES/heat_2.JPG")

Study of the main competitors in Phoenix

List_restaurants2.head(10)

	business_id	Number of reviews	frequency	latitude	longitude
0	gQMAcDm8kv8ev7x2BshMwg	944	6	33.510181	-112.096168
1	JzOp695tclcNCNMuBl7oxA	2473	6	33.419578	-111.915885
2	85o8XAZDoPmRqHFhdJLAGg	343	6	33.478016	-112.254559
3	PhMfnMldVKXo7HqHw6DSrg	372	5	33.394818	-111.940576
4	OgJ0KxwJcJ9R5bUK0ixCbg	2284	5	33.500597	-112.074237
5	thLX_k20SPJ0KyusGTBIHw	1485	5	33.499386	-111.926570
6	eS29S_06lvsDW04wVrIVxg	1570	5	33.479016	-112.047963
7	U1ZVgF-kfkvv_rcoe0RglQ	313	5	33.488760	-112.082402
8	cTJjTKz2huGZ-ElScC2pSw	1280	5	33.513575	-112.073381
9	wl0QZqAzr1DelslQ02JGCQ	1728	4	33.423059	-111.951843

competitor1=restaurants_Phoenix[restaurants_Phoenix['business_id']=='JzOp695tclcNCNMuBl7oxA']

competitor1.head()

	RestaurantsTakeOut	BusinessAcceptsCreditCards	NoiseLevel	GoodForKids	RestaurantsReservations	RestaurantsGoodForGroups	RestaurantsPriceRange2	HasTV	Alcohol	BikeParking	...	is_open	latitude	longitude	name	postal_code	review_count	stars	state	metropolis	distance metropolis
4115	1	1	1	1	1	1	2	1	2	1	...	1	33.419578	-111.915885	Four Peaks Brewing	85281	2400	4.5	AZ	Phoenix	18.964006

1 rows × 68 columns

Four_Peaks_Brewing=restaurants_Phoenix[restaurants_Phoenix['name']=='Four Peaks Brewing']

Four_Peaks_Brewing.head()

	RestaurantsTakeOut	BusinessAcceptsCreditCards	NoiseLevel	GoodForKids	RestaurantsReservations	RestaurantsGoodForGroups	RestaurantsPriceRange2	HasTV	Alcohol	BikeParking	...	is_open	latitude	longitude	name	postal_code	review_count	stars	state	metropolis	distance metropolis
4115	1	1	1	1	1	1	2	1	2	1	...	1	33.419578	-111.915885	Four Peaks Brewing	85281	2400	4.5	AZ	Phoenix	18.964006

1 rows × 68 columns

This first competitor only has one restaurant in Phoenix

competitor2=restaurants_Phoenix[restaurants_Phoenix['business_id']=='85o8XAZDoPmRqHFhdJLAGg']
competitor2.head()

	RestaurantsTakeOut	BusinessAcceptsCreditCards	NoiseLevel	GoodForKids	RestaurantsReservations	RestaurantsGoodForGroups	RestaurantsPriceRange2	HasTV	Alcohol	BikeParking	...	is_open	latitude	longitude	name	postal_code	review_count	stars	state	metropolis	distance metropolis
1611	1	1	-1	1	1	1	2	1	1	1	...	1	33.478016	-112.254559	Song Lynn	85037	332	4.5	AZ	Phoenix	0.0

1 rows × 68 columns

Song_Lynn=restaurants_Phoenix[restaurants_Phoenix['name']=='Song Lynn']
Song_Lynn.head()

	RestaurantsTakeOut	BusinessAcceptsCreditCards	NoiseLevel	GoodForKids	RestaurantsReservations	RestaurantsGoodForGroups	RestaurantsPriceRange2	HasTV	Alcohol	BikeParking	...	is_open	latitude	longitude	name	postal_code	review_count	stars	state	metropolis	distance metropolis
1611	1	1	-1	1	1	1	2	1	1	1	...	1	33.478016	-112.254559	Song Lynn	85037	332	4.5	AZ	Phoenix	0.0

1 rows × 68 columns

competitor3=restaurants_Phoenix[restaurants_Phoenix['business_id']=='gQMAcDm8kv8ev7x2BshMwg']
competitor3.head()

	RestaurantsTakeOut	BusinessAcceptsCreditCards	NoiseLevel	GoodForKids	RestaurantsReservations	RestaurantsGoodForGroups	RestaurantsPriceRange2	HasTV	Alcohol	BikeParking	...	is_open	latitude	longitude	name	postal_code	review_count	stars	state	metropolis	distance metropolis
1191	1	1	0	1	0	1	1	1	0	0	...	1	33.510181	-112.096168	Pho Thanh	85015	898	4.0	AZ	Phoenix	0.0

1 rows × 68 columns

Pho_Thanh=restaurants_Phoenix[restaurants_Phoenix['name']=='Pho Thanh']
Pho_Thanh.head()

	RestaurantsTakeOut	BusinessAcceptsCreditCards	NoiseLevel	GoodForKids	RestaurantsReservations	RestaurantsGoodForGroups	RestaurantsPriceRange2	HasTV	Alcohol	BikeParking	...	is_open	latitude	longitude	name	postal_code	review_count	stars	state	metropolis	distance metropolis
1191	1	1	0	1	0	1	1	1	0	0	...	1	33.510181	-112.096168	Pho Thanh	85015	898	4.0	AZ	Phoenix	0.0

1 rows × 68 columns

Same for Pho Thanh

Let's now only consider the Vietname restaurants in Phoenix that have more than 4 stars and that are inside the list of restaurants visited by our target users. These restaurants are the main Competitors of Banh Mi Boys.

Vietnamese_Phoenix=restaurants_Phoenix[restaurants_Phoenix['categories'].str.contains('Vietnamese')]
Vietnamese_Phoenix2=Vietnamese_Phoenix[Vietnamese_Phoenix['stars']>3.5]
Main_competitors=Vietnamese_Phoenix2[Vietnamese_Phoenix2['business_id'].isin(List_business_id2)]
Main_competitors.head()

	RestaurantsTakeOut	BusinessAcceptsCreditCards	NoiseLevel	GoodForKids	RestaurantsReservations	RestaurantsGoodForGroups	RestaurantsPriceRange2	HasTV	Alcohol	BikeParking	...	is_open	latitude	longitude	name	postal_code	review_count	stars	state	metropolis	distance metropolis
137	1	1	0	1	0	1	1	1	0	1	...	1	33.611452	-112.101480	Pho Vn Vietnamese Cuisine	85023	96	4.0	AZ	Phoenix	0.0
803	1	1	0	1	1	1	2	1	2	1	...	1	33.472680	-112.064864	Rice Paper on 7th	85006	180	4.0	AZ	Phoenix	0.0
1073	1	1	0	1	0	1	1	0	0	0	...	1	33.479464	-112.152133	PHO 43	85009	565	4.5	AZ	Phoenix	0.0
1191	1	1	0	1	0	1	1	1	0	0	...	1	33.510181	-112.096168	Pho Thanh	85015	898	4.0	AZ	Phoenix	0.0
1378	1	1	0	1	1	1	1	1	0	1	...	1	33.800185	-112.128133	Pho Saigon Pearl	85086	141	4.0	AZ	Phoenix	0.0

5 rows × 68 columns

# Phoenix location
lat_Phoenix = 33.4483771
lon_Phoenix = -112.0740373

m_Phoenix = folium.Map([lat_Phoenix, lon_Phoenix], zoom_start=10)

for index, row in Main_competitors.iterrows():
    folium.CircleMarker([row['latitude'], row['longitude']],
                        radius=5,
                        fill_color="#3db7e4", # divvy color
                       ).add_to(m_Phoenix)
    
# convert to (n, 2) nd-array format for heatmap
matrix = Main_competitors[['latitude', 'longitude']].as_matrix()

# plot heatmap
m_Phoenix.add_children(plugins.HeatMap(matrix, radius=15))


    
m_Phoenix

C:\Users\hecto.DESKTOP-ACA6A3T\Anaconda3\lib\site-packages\ipykernel_launcher.py:14: FutureWarning:

Method .as_matrix will be removed in a future version. Use .values instead.

C:\Users\hecto.DESKTOP-ACA6A3T\Anaconda3\lib\site-packages\ipykernel_launcher.py:17: FutureWarning:

Method `add_children` is deprecated. Please use `add_child` instead.

Image("C:/Users/hecto.DESKTOP-ACA6A3T/Documents/DENMARK/BANALYTICS/2semester/Advance BA/Final Project/FINAL FILES/heat_3.JPG")

As we can see, the competitors are less present in the three areas where the demand for restaurants is really high. Near Scottsdale, Phoenix and Tempe.